class_docstr: Utility class that uses DHT to find most suitable experts for RemoteMixtureOfExperts.
  Each expert has an identifier in the form of {prefix}.{i}.{j}.{...}, e.g. "ffn_expert.98.76.54.32.10"
  An expert identifier consists of:
  
      * optional prefix that determines expert role, experiment name, etc.
      * one or more integers that determine that expert's position in an N-dimensional grid
  
  A hivemind.moe.Server can ``declare_experts(dht, expert_uids: List[str])`` to make its experts visible to everyone.
  When declaring experts, DHT will store each expert's uid and all its prefixes until :expiration: (specified at init)
  For instance, declaring "ffn_expert.98.76.54.32.10" will store the following keys in a DHT:
  ``"ffn_expert.98", "ffn_expert.98.76", "ffn_expert.98.76.54", ..., "ffn_expert.98.76.54.32.10"``
  
  In order to enable fast beam search, DHT maintains dictionaries of all active suffixes for every prefix
  (e.g. "ffn_expert.98": {76: ffn_expert.98.76...., 123: ffn_expert.98.123..., 225: ffn_expert.98.225....}))
  
  RemoteMixtureOfExperts can use these prefixes to find top-k most suitable experts with a left-to-right beam search.
  For instance, consider RemoteMixtureOfExperts with prefix "ffn_expert" and grid size [100, 100, 100, 100, 100].
  This MoE can query all experts with that prefix and arbitrary indices in 0...99 along each dimension.
  However, not every expert in such 100^5 grid can be alive at a given moment of time (the grid size is redundant).
  In order to find k best "alive" experts, MoE first ranks indices along the first dimension with its gating function.
  It can then check which of those indices correspond to "alive" experts by querying keys such as "ffn_expert.98".
  
  After selecting k best indices along first dimension, MoE moves to the second dimension.
  It can find top-k index pairs (e.g. "expert.98.76") that use one of k best indices from the previous step.
  This beam search explores one additional dimension per step and finds k best experts from across the DHT
  in O(k * num_dimensions * dimension_size) time depending on the chosen grid dimensions.
  
  :param dht: a running DHT daemon that is used for beam search AND local caching
  :param uid_prefix: search for experts whose uids start with this prefix
  :param grid_size: dimensions that form expert uid (see above)
  :param num_workers: number of concurrent DHT coroutines per beam search
  :param negative_caching: if True, whenever DHT is unable to find an expert or prefix, it will cache the "no key"
    result inside the DHT for :expiration: seconds. Caching only affects beam search and has three main effects:
  
    1. Faster beam search under node failures: if there are inconsistencies in DHT keys, such as a prefix pointing to
       a now-defunct expert, these inconsistencies will be overwritten by the first peer that stumbles upon them. As a
       result, beam search will not have to wait for non-existent experts until the expiration of their DHT entries;
    2. Delayed expert availability: Without negative cache, new experts are always immediately available for beam
       search after they are published to the DHT. With negative cache, there are rare cases (e.g. when adding new
       experts in place of recently defunct ones) when new experts will be initially invisible, but gradually become
       visible to more peers as those peers refresh their cache. This process takes at most :expiration: seconds;
    3. Faster beam search in very sparse grids: there is one edge case where negative cache will improve beam search
       performance; If an expert grid is very sparse, there can be empty indices in the first grid dimension (i.e.
       indices {i} such that _no_ experts that start with "{prefix}.{i}.*"). If so, the default beam search will
       be very slow due to the way it forms initial beam. Beam search with negative cache enabled will run normally.
       Though, this is a pathological case (e.g. only 90 experts in an oversized 100x100 grid) that should be avoided.
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docstr: iterate over all exemplar experts attached to current beam
docstr_tok: ["iterate","over","all","exemplar","experts","attached","to","current","beam"]
code: def _iterate_matching_experts(
      beam: List[
          Tuple[Score, ExpertPrefix, Dict[Coordinate, ExpertInfo]]
      ],
      grid_scores: Sequence[Sequence[float]],
  ) -> Iterator[Tuple[Score, ExpertInfo]]:
      """iterate over all exemplar experts attached to current beam"""
      for score, prefix, suffixes in beam:
          for next_coord, match in suffixes.items():
              if len(grid_scores) == 1 and next_coord == FLAT_EXPERT:
                  yield score, match
              elif isinstance(match.uid, ExpertUID) and match.uid.count(
                  UID_DELIMITER
              ) == len(grid_scores):
                  expert_coords = match.uid.split(UID_DELIMITER)[1:]
                  if all(
                      coord.isdigit()
                      and 0 <= int(coord) < len(grid_scores[i])
                      for i, coord in enumerate(expert_coords)
                  ):
                      expert_score = sum(
                          scores[coord]
                          for scores, coord in zip(
                              grid_scores, map(int, expert_coords)
                          )
                      )
                      yield expert_score, match
                  else:
                      logger.warning(
                          f"Found incompatible expert coordinates: {expert_coords}"
                      )
              else:
                  logger.warning(
                      f"Found incompatible expert UID: {match.uid}"
                  )
  
file_path: hivemind/hivemind/moe/client/beam_search.py
imports: import asyncio
  import heapq
  from collections import deque
  from functools import partial
  from typing import Deque, Dict, Iterator, List, Optional, Sequence, Set, Tuple, Union
  from hivemind.dht import DHT, DHTNode
  from hivemind.moe.client.expert import RemoteExpert, batch_create_remote_experts, create_remote_experts
  from hivemind.moe.expert_uid import FLAT_EXPERT, PREFIX_PATTERN, UID_DELIMITER, Coordinate, ExpertInfo, ExpertPrefix, ExpertUID, Score, is_valid_prefix, is_valid_uid
  from hivemind.p2p import PeerID
  from hivemind.utils import DHTExpiration, MPFuture, ValueWithExpiration, get_dht_time, get_logger
label_desc: Use image node_id 6 for calling the MoEBeamSearcher obj's underlying member method code with example usage: obj._iterate_matching_experts(beam, grid_scores) without return types